Objective of AI processes is to teach machines from experience by feeding the right information and doing self-correction. Machine Learning is a subsection of AI that devices means by which systems can automatically learn and improve from experience. ML involves observing and studying data or experiences to identify patterns and set up a reasoning system based on the findings.
Create model that fits a business use case
Train model with business data to train the model,
Use trained model to create custom business solution for a client using AI/ML capability
We leverage myriads of python libraries to handle many real life problem using AI/ML.
AI is applied in analytics to build predictions that can help people create strong strategies and look for more effective solutions. FinTech applies AI in investment platforms to do market research and predict where to invest funds for bigger profits. The traveling industry uses AI to deliver personalized suggestions or launch chatbots, plus enhance the overall user experience. These examples show that AI and ML are used process loads of data to offer better user experience, more personal and accurate one.
we leverage Azure Machine Learning (Azure ML) services which is a cloud-based service for creating and managing machine learning solutions. Also Azure AutoML,a cloud-based service, is used to automate building machine learning pipelines for classification, regression and forecasting tasks. Its goal is not only to tune hyper-parameters of a given model, but also to identify which model to use and how to pre-process the input dataset.
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